It’s no longer enough for your data to be big. Today, data needs to be deep, too. Here’s why deep data is so essential for enterprise data analytics, and tips for making your data deep.
These days, anyone can collect lots of data. Data collection can be easily automated, and data storage is cheap.
In fact, because we live in an age when everything is digitized, it’s virtually impossible not to collect lots of information. From network switches to remote sensors to customers’ browsing history, everything spits out data at a dizzying pace – and companies need to make sense of that data if they want to understand the trends that power their business.
Yet simply collecting lots of data is not enough. Large-scale data collection gives you big data – meaning a large volume of data to analyze – but it doesn’t necessarily mean you have data that is valuable.
To be valuable, your data needs to be not just big data, but also “deep” data. That means it has to be high-quality, actionable information.
Data that is collected haphazardly is unlikely to have these characteristics. No matter how big the amount of data you collect, you can’t derive much value from it if you are not able to analyze it rapidly to glean accurate, reliable information.
Generating deep data can be tough for two main reasons.
First, data quality tends to vary widely. Information might be missing, inaccurate or inconsistent within a database.
For example, consider the data quality challenges you face when collecting information about visitors to a website. Parts of the data you collect about the technology used by your visitors is likely to be incomplete because some users will be using browsers or operating systems that cannot be identified.
The data is also likely to contain inaccuracies. For instance, if a customer uses a virtual private network (VPN) to mask his or her geographic location, the data you collect about the geographic origins of website users will not be completely accurate.
Last but not least, the data will be inconsistent if you have collected more information on some users than on others. That could happen if, for example, not all users spend the same amount of time on the site.